When are they useful?

• Handling hierarchical or grouped data (e.g., students within classrooms, patients within clinics) (Lee and Nelder 1996)

• Modeling non-normal outcomes, such as:

  • Binary outcomes (using logistic GLMMs) (Wang et al. 2017)

  • Count data (using Poisson or negative binomial GLMMs) (Candy 2000)

  • Proportions or rates (Salinas Ruı́z et al. 2023)

Application to Social Science

• We are investigating the potential the dangers of a Post-Roe v. Wade world upon mothers, and the ongoing systematic issues that lay with women of color when giving birth.
• We will be using that statistical method, Generalized linear mixed model(GLMM), to help analyze the potential risks that lay with mothers that give birth in the United States.
• With our results we may hope to be better able to identify the relationships between maternal mortality and explanatory variables such as age group, ethnicity, and the Dobbs era of which these deaths took place.
• These findings are relevant because we could use them as evidence to help reduce maternal mortality amongst certain groups and potentially investigate if a post-Dobbs era could have resulted in a higher maternal mortality rate.

Math Background

Let

• \(\mathbf{y}\) be a \(Nx1\) column vector outcome variable

• \(\mathbf{X}\) be a \(Nxp\) matrix for the \(p\) predictor variables

• \(\boldsymbol{\beta}\) be a \(px1\) column vector of the fixed effects coefficients

• \(\mathbf{Z}\) is a \(Nxq\) matrix of the \(q\) random effects

• \(\mathbf{u}\) is a \(qx1\) vector of random effects, and

• \(\boldsymbol{\epsilon}\) is a \(Nx1\) column vector of the residuals

Then the general equation for the model is given by:

\[\mathbf{y}=\mathbf{X}\boldsymbol{\beta}+\mathbf{Z}{u}+\boldsymbol{\epsilon}\]

Math Background

GLMMs typically include a link function that relates the response variable \(\mathbf{y}\) to a linear predictor, \(\eta\), which excludes the residuals. So then \[\boldsymbol{\eta}=\mathbf{X}\boldsymbol{\beta}+\mathbf{Z}\boldsymbol{\lambda}\]

The link function is \(g(\cdot)\), where \[g(E(\mathbf{y}))=\boldsymbol{\eta}\] where \(E(\mathbf{y})\) is the expectation of \(\mathbf{y}\). The choice of link function depends on the outcome distribution. For this paper our data demonstrates a Negative Binomial distribution for overdispered count data, so we will use a log link function.
\[g(\cdot)=log_e(\cdot)\]

About the Dataset

• Vital Statistics Rapid Release (VSRR) Provisional Maternal Death Counts and Rates, in the form of a .csv

• Published by National Vital Statistics System, a collaboration between the National Center for Health Statistics (NCHS) and state vital record offices

• Monthly death counts and death rates by race/ethnicity, age, and overall

• Data from January 2019 to December 2024

• Data is provisional and updated quarterly; becomes more reliable with more updates

• Maternal Deaths between 1 and 9 are suppressed for privacy reasons

Missing Data

• “Native Hawaiian or Other Pacific Islander, Non-Hispanic” has 70 NAs for Maternal Mortality, omitting this subgroup entirely

• “American Indian or Alaska Native, Non-Hispanic” has 58 NAs for Maternal Mortality Rate, not using rate in our model, omitting will not affect modeling

Models

Model Choice

Our Chosen model in Regression equation format:

\[ \begin{align*} \log(\mathbb{E}[\text{Maternal Deaths}_i]) &= \beta_0 + \beta_1 \cdot \text{Black}_i \\ &+ \beta_2 \cdot \text{White}_i + \beta_3 \cdot \text{Hispanic}_i \\ &+ \beta_4 \cdot \text{American Indian or Alaska Native}_i \\ &+ \beta_5 \cdot \text{EthnicityUnknown}_i \\ &+ \beta_6 \cdot \text{Age 25-39}_i + \beta_7 \cdot \text{Age 40 Plus}_i \\ &+ \beta_8 \cdot \text{Post Dobbs}_i + b_{\text{Year}[i]} + \log(\text{Live Births}_i) \end{align*} \]

Assumption Checking

• Our chosen model adheres by all assumptions of GLMM
• The response variable and the predictors have a linear relationship within the levels of random effects.

Assumptions Checking

• The random effects are assumed to be normally distributed, with mean 0 and variance \(\sigma\).
• The random effect of year is approximately normal with some mild deviations which is common with count models.

Model Outcomes

• Black women had a 267% higher rate of maternal mortality compared to reference group (Asian), holding all other groups constant
• White women have a 31% higher rate of maternal mortality compared to reference group (Asian) holding all other groups constant
• Hispanic women have a 15% higher rate of maternal mortality compared to reference group (Asian), holding all other groups constant
• Unknown Ethnicity was not statistically significant
• Women 25-39 have a 37% higher rate of maternal mortality compared to reference group(Under 25) holding all other groups constant
• Women 40 years and older have a 505% higher rate of maternal mortality compared to reference group (under 25), holding all other groups constant

Model Outcomes

• There was a 20% lower rate in maternal mortality after Dobbs was passed compared to prior the passing of Dobbs
• The residual variance is 7.99, variance in maternal deaths not explained by fixed effects or random effects 5.5% of proportion of variance explained by fixed effects only, they only explain 5.5% of variance in maternal deaths conditional 5.9% is the proportion of variance contributed by fixed and random effects, random effect year contributes little to improving model
• Our low variability could be due to not having a random effect like state or other fixed effects to explain variable

Limitations

• The data used is provisional and updates quarterly with both new and old counts, so further analysis may offer differing results

• The data offered counts by age group and ethnicity, but not both (i.e. maternal deaths for black women 40 and over). The inclusion of such data would give a better indication of the relationship between the two subgroups.

• Due to the Covid-19 pandemic’s impact on the healthcare system, access to regular healthcare was restricted. This likely had an impact on maternal mortality and may partially account for increased rates from 2020-2023.

• More variables in the dataset would offer a better picture of the predictors of maternal mortality, specifically in regards to their relationship with ethnicity. Prenatal health, healthcare access, abortion access, and other prenatal behaviors would be useful.

Conclusions and Further Study

• American Indian or Alaska Natives have the highest maternal mortality rate, despite having the smallest number of maternal deaths
• Black women had the second highest maternal mortality rate, and the second highest total maternal deaths
• Based on the provisional data there is no evidence that maternal mortality rate increased post-Dobbs
• A study utilizing state specific data (due to differing laws regarding abortion) might give a clearer indication of the impact of Dobbs on maternal mortality and whether abortion access is a factor
• If this GLMM could be improved upon with new data then racial disparities in maternal mortality could be reduced by identifying significant explanatory variables
• A study investigating healthcare access for expecting mothers specific to the Covid-19 pandemic would give insight into the spike in maternal mortality rate in 2022